In this work, ZnO nanostructures for powder ZnO were synthesized by Hydrothermal Method. Size and shape of ZnO nanostructureas can be controlled by change ammonia concentration. In the preparation of ZnO nanostructure, zinc nitrate hexahydrate [Zn(NO₃)₂·6H2O] was used as a precursor. The structure and morphology of ZnO nanostructure have been characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction (XRD). The synthesized ZnO nanostructures have a hexagonal wurtzite structure. Also using Zeta potential and Particle Size Analyzers and size distribution of the ZnO powder

Nanosponges (NS) of etodolac(ETO) was prepared using the emulsion solvent diffusion method ; the effects of drug: polymer ratio, the effect of level concentration of internal phase and stirring time and other variables that effect on the physical characteristics of NS were investigated and characterized, The selected formula was lyophilized then incorporated into hydrogel ; which also evaluated .The results show that the formulation that contain Drug: PVA:EC in ratio 1:3:2 is the best with smallest particle size 40.2±0.098 with polydispersibility0.005 and in vitro release 97.6±0.11%, , ETO NS Carbopol hydrogel produced a significant(p<0.05) improvement of the in vitro release than pure ETO hydrogel.

The aim of this study was to compare the effect of conventional implant site preparation technique and a combination of conventional/piezosurgery preparation on implant stability measured at different time intervals, insertion torque, and preparation time. A randomized controlled study was designed, it included 26 patients who received 54 dental implants randomly assigned to 2 groups; in the control group, implants were installed after conventional preparation with drills whereas the study group received implants after mixed conventional/piezosurgery preparation. The outcome variables included: implant stability measured immediately after implant insertion, at 8 weeks and 16 weeks postoperatively, insertion torque and preparation time. All

The effective surface area of drug particle is increased by a reduction in the particle size. Since dissolution takes place at the surface of the solute, the larger the surface area, the further rapid is the rate of drug dissolution. Ketoprofen     is class II type drug according to (Biopharmaceutics Classification System BCS) with low solubility and high permeability. The aim of this investigation was to increase the solubility and hence the dissolution rate by the preparation of ketoprofen     nanosuspension using solvent evaporation method. Materials like PVP K30, poloxamer 188, HPMC E5, HPMC E15, HPMC E50, Tween 80 were used as stabilizers in perpetration of differ

In this study we examine variations in the structure of perovskite compounds of LaBa2Cu2O9, LaBa2CaCu3O12 and LaBa2Ca2Cu5O15 synthesized using the solid state reaction method. The samples’ compositions were assessed using X-ray fluorescence (XRF) analysis. The La: Ba: Ca: Cu ratios for samples LaBa2Cu2O9, LaBa2CaCu3O12 and LaBa2Ca2Cu5O15 were found by XRF analysis to be around 1:2:0:2, 1:2:1:3, and 1:2:2:5, respectively. The samples’ well-known structures were then analyzed using X-ray diffraction. The three samples largely consist of phases 1202, 1213, and 1225, with a trace quantity of an unknown secondary phase, based on the intensities and locations of the diffraction peaks. According to the measured parameters a, b, and c, every sa

The mixed ligands complexes of 8-hydroxyquinoline and Schiff base 1, 5-dimethyl-4-(5- oxohexan-2-ylideneamino)-2-phenyl-1H-pyrazol-3 (2H)-one (L) with Cr(III) , Mn(II), Fe(II), Co(II) , Ni(II), Cu(II) and Zn(II) ions were prepared. The compounds have been characterized by NMR, FT-IR, UV-Vis and mass spectra, elemental microanalysis (C.H.N.), magnetic moment, chloride contain, atomic absorption and molar conductance. All prepared complexes were octahedral geometry. Compound structures treated theoretically using the program hyper chem. 8 in gas phase at 298°K. The compounds were also screened for their bioactive property such as antifungal and antibacterial.

A simple, cheap, fast, accurate, Safety and sensitive spectrophotometric method for the determination of sulfamethaxazole (SFMx), in pure form and pharmaceutical dosage forms. has been described The Method is based on the diazotization of the drug by sodium nitrite in acidic medium at 5Cº followed by coupling with salbutamol sulphate (SBS) drug to form orange color the product was stabilized and measured at 452 nm Beer’s law is obeyed in the concentration range of 2.5-87.5 ?g ml-1 with molar absorptivity of 2.5x104 L mole-1 cm-1. All variables including the reagent concentration, reaction time, color stability period, and sulfamethaxazole /salbutamol ratio were studied in order to optimize the reaction conditions. No interferences were

In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St